基于WOA-RF的边坡稳定性预测模型

张建涛 刘志祥 张双侠 郭腾飞 袁丛祥

张建涛, 刘志祥, 张双侠, 郭腾飞, 袁丛祥. 基于WOA-RF的边坡稳定性预测模型[J]. 高压物理学报, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837
引用本文: 张建涛, 刘志祥, 张双侠, 郭腾飞, 袁丛祥. 基于WOA-RF的边坡稳定性预测模型[J]. 高压物理学报, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837
ZHANG Jiantao, LIU Zhixiang, ZHANG Shuangxia, GUO Tengfei, YUAN Congxiang. Slope Stability Prediction Based on WOA-RF Hybrid Model[J]. Chinese Journal of High Pressure Physics, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837
Citation: ZHANG Jiantao, LIU Zhixiang, ZHANG Shuangxia, GUO Tengfei, YUAN Congxiang. Slope Stability Prediction Based on WOA-RF Hybrid Model[J]. Chinese Journal of High Pressure Physics, 2024, 38(3): 035301. doi: 10.11858/gywlxb.20230837

基于WOA-RF的边坡稳定性预测模型

doi: 10.11858/gywlxb.20230837
基金项目: 国家重点研发计划项目(2022YFC2904101);国家自然科学基金(52374107,51974359)
详细信息
    作者简介:

    张建涛(2000-),男,硕士研究生,主要从事机器学习与边坡稳定性研究. E-mail:215512079@csu.edu.cn

    通讯作者:

    刘志祥(1967-),男,博士,教授,主要从事采矿工程与岩石力学研究. E-mail:liulzx@csu.edu.cn

  • 中图分类号: O347; TU457

Slope Stability Prediction Based on WOA-RF Hybrid Model

  • 摘要: 为有效地预测边坡稳定性和预防边坡失稳事故的发生,提出了鲸鱼优化算法(whale optimization algorithm,WOA)和随机森林(random forest,RF)相结合的混合模型WOA-RF;基于所收集的边坡案例,采用混淆矩阵的分类性能指标和受试者工作特征曲线及线下面积评估混合模型WOA-RF的分类和泛化性能;使用WOA对4种广泛应用的机器学习模型进行优化,并将优化后的机器学习模型与WOA-RF模型进行对比分析。结果表明:WOA可以有效地优化超参数和提升模型性能;最优WOA-RF模型在训练集和测试集上的准确率分别为0.99和0.94,优化后,准确率、精确率、召回率、精确率和召回率的加权平均值分别提升了11.9%、19.0%、4.8%和11.9%;对比分析各个模型的预测性能后发现,WOA-RF模型的各项指标均优于其他模型;确定了特征重要性排序,发现容重是影响边坡稳定性的最敏感特征。WOA-RF模型可有效地预测边坡稳定性,预测结果可为防护措施的制定提供依据。

     

  • 图  边坡特征参数

    Figure  1.  Slope parameters

    图  特征散点分布和相关系数

    Figure  2.  Scatter distribution and correlation coefficient of dataset

    图  混淆矩阵

    Figure  3.  Confusion matrix

    图  交叉验证和适应度函数计算步骤

    Figure  4.  Cross validation and fitness function calculation process

    图  WOA-RF模型构建流程

    Figure  5.  WOA-RF model construction process

    图  不同种群数量下优化过程中适应度的变化曲线

    Figure  6.  Fitness variation for different population size during optimization process

    图  WOA-RF模型在训练集和测试集上的混淆矩阵和分类性能指标

    Figure  7.  Confusion matrix and classification performance of WOA-RF model for training and test sets

    图  WOA-RF模型在训练集和测试集上的ROC曲线

    Figure  8.  ROC curves of WOA-RF model for training and test sets

    图  各模型在测试集上的ROC曲线

    Figure  9.  ROC curves of different models for the test set

    图  10  各模型分类性能指标雷达图

    Figure  10.  Radar chart of performance indicators for different models

    图  11  特征重要性分值(重复次数:100)

    Figure  11.  Feature importance score (iteration: 100)

    表  1  数据集描述性统计

    Table  1.   Data set descriptive statistics

    Feature γ/(kN∙m−3) c/kPa ϕ/(°) Φ/(°) H/m ru/kPa S
    Max 31.30 300.00 45.00 59.00 511.00 0.50 1.0
    Min 12.00 0 0 16.00 3.60 0 0
    Mean 21.76 34.12 28.73 36.10 104.19 0.22 0.5
    Median 20.96 19.96 30.24 35.00 50.00 0.25
    Standard deviation 4.15 45.96 10.61 10.25 133.08 0.16
    下载: 导出CSV

    表  2  WOA-RF模型参数设置

    Table  2.   Parameter settings in WOA-RF model

    Title nestimators Nmax_features Dmax_depth Nmin_samples_leaf Nmin_samples_split Criterion
    Range [1, 500] [1, 6] [1, 30]
    Optimized value 167 3 11 1 2 Gini
    下载: 导出CSV

    表  3  各模型优化前后的$A_{\mathrm{cc}} $$F_{\mathrm{1\text{-}score}}$

    Table  3.   $A_{\mathrm{cc}} $ and $F_{\mathrm{1\text{-}score}}$ before and after optimization

    Model Acc F1-score Model Acc F1-score
    KNN 0.76 0.76 WOA-KNN 0.82 0.82
    SVM 0.80 0.80 WOA-SVM 0.85 0.84
    ANN 0.53 0.52 WOA-ANN 0.80 0.79
    DT 0.71 0.71 WOA-DT 0.75 0.75
    RF 0.84 0.84 WOA-RF 0.94 0.94
    下载: 导出CSV

    表  4  各模型优化后分类性能指标及排名

    Table  4.   Classification performance and ranking of models after optimization

    ModelAccAcc rankPrePre rankReRe rankF1-scoreF1-score rankTotal score
    WOA-KNN0.8230.8220.8230.82311
    WOA-SVM0.8440.8540.8440.84416
    WOA-ANN0.8020.8330.7620.7929
    WOA-DT0.7510.7510.7210.7314
    WOA-RF0.9451.0050.8850.94520
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-25
  • 修回日期:  2024-01-19
  • 录用日期:  2024-01-26
  • 刊出日期:  2024-06-03

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